Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation

Yiwei Liu, Le Wang, Lin Feng and Bo Jin
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Yiwei Liu: School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China
Le Wang: College of Digital Technology and Engineering, Ningbo University of Finance and Economics, 899 Xueyuan Road, Haishu District, Ningbo 315175, China
Lin Feng: School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China
Bo Jin: School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China

Mathematics, 2020, vol. 9, issue 1, 1-22

Abstract: Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets. The performance of these algorithms depends on the efficiency of both steps, both of which are usually time-consuming. In this study, we propose an efficient pattern-growth based HUI mining algorithm, called tail-node tree-based high-utility itemset (TNT-HUI) mining. This algorithm avoids the time-consuming candidate generation step, as well as the need of scanning the original dataset multiple times for exact utility values, as supported by a novel tree structure, named the tail-node tree (TN-Tree). The performance of TNT-HUI was evaluated in comparison with state-of-the-art benchmark methods on different datasets. Experimental results showed that TNT-HUI outperformed benchmark algorithms in both execution time and memory use by orders of magnitude. The performance gap is larger for denser datasets and lower thresholds.

Keywords: utility mining; tail-node tree; efficient algorithm; market-basket analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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